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Bioinformatics-Driven Discovery of Signaling Pathways and Genes Influencing Cervical Cancer

SN Computer Science 2024年第8期5卷 989页

作者： Ali, Anooja Mohan, Jinu Nadaf, Tousif Ahamed Allabksha Ravishankar, H. Deepa, K.R. School of CSE REVA University Karnataka Bangalore India Department of Artificial Intelligence and Cyber Security Ilahia College of Engineering and Technology Kerala Ernakulam India Wipro Arabia Ltd. Dhahran Saudi Arabia School of Computing and Information Technology REVA University Karnataka Bangalore India School of Electrical and Electronics Engineering REVA University Karnataka Bangalore India

Cervical cancer is a threat to women worldwide, with poor early prognosis. Cervical cancer is strongly linked to human papillomavirus (HPV), particularly high-risk HPV16 and HPV18, a prevalent gynecological malignancy. It ranks fourth among women's malignant diseases in terms of morbidity and mortality and there has been a rise in the incidence of cervical cancer among young females in recent decades. The Gene Expression Omnibus database (GEO) was used to screen and download three gene expression profiles (GSE63514, GSE6791, and GSE9750). The study used GEO2R and Venn diagrams to identify differentially expressed genes (DEGs) across three gene expression datasets. GO and KEGG Pathway Enrichment Analysis (PEA) were then performed on the DEGs to understand their biological functions and signaling pathways. Gene set enrichment analysis (GSEA) is also applied to analyze expression profiles. Additionally, a protein–protein interaction (PPI) network is constructed using the DEGs, which underwent a functionality-based enrichment investigation to identify hub genes. The proposed approach has clinical relevance in uncovering potential therapeutic targets and improving our understanding of the prognosis and treatment of the disease. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cervical cancer Differentially expressed genes Expression profile Gene ontology Human papillomavirus

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Revolutionizing Lion Conservation with EfficientNet B7: Real-Time Detection and Health Classification

Revolutionizing Lion Conservation with EfficientNet B7: Real...

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Automation, computing and Renewable Systems (ICACRS), International Conference on

作者： Sampath Kumar Koradaa Syam Virat Gupta Potnuru C. Ashok Kumar C. Jothi Kumar Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This research introduces a cutting-edge framework leveraging advanced AI and IoT technologies to revolutionize lion conservation. The primary focus is on real-time lion detection, individual identification using unique facial features, gender classification, and health condition assessment, including malnutrition, injuries, and illnesses. By integrating GPS modules and IoT sensors, the system facilitates noninvasive and ethical monitoring of lions and their habitats. The study employs YOLO (You Only Look Once) for real-time detection of lions in video feeds and EfficientNet B7 for multitask classification, enabling precise identification of individual lions and their health status. DeepSORT is utilized for tracking lion movements over time, ensuring continuous observation and territorial mapping. These models are deployed on edge devices, such as drones and camera traps, to deliver instantaneous results while minimizing latency. The proposed system offers numerous advantages, including improved conservation efforts through real-time alerts and actionable insights. Its ability to monitor health conditions and movement patterns enables proactive interventions to safeguard lion populations. Moreover, the integration of IoT sensors ensures comprehensive environmental monitoring, supporting a holistic understanding of the ecosystem. By employing cloud platforms for data analytics and storage, the system provides accessible dashboards and detailed reports, empowering conservationists and wildlife authorities with valuable insights. This framework not only enhances the efficiency of lion conservation but also ensures ethical practices, setting a benchmark for integrating AI and IoT in wildlife preservation. The research underscores the potential of technology to address critical ecological challenges and promote sustainable conservation efforts.

关键词： Biometrics YOLO Accuracy Biological system modeling Wildlife Habitats Real-time systems Sensors Environmental management Injuries

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Air Quality Prediction Using Adaboost

Air Quality Prediction Using Adaboost

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2023 Intelligent computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Thamizhselvi, D. Kasi, Baskar Kamalakkannan, K. Bharath, S. Gowtham, S. Kishore, M.A. Sri Sairam Engineering College Department of Information Technology Chennai India School of Computing Science and Engineering Galgotias University Greater Noida India Srm Institute of Science and Technology Department of Automobile Engineering Chennai India

ISBN: (纸本)9798350394580

The survival of human beings is contingent upon the presence of air. The detrimental impact of contemporary culture on air quality is seen in nearly all regions. Pollution of the environment is caused by activities in the industrial, transit, and residential sectors. The monitoring and prediction of air quality have become imperative in contemporary times, particularly in developing nations such as India. The most efficient techniques for assessing contemporary threats are thought to be prediction technologies based on machine learning. This study looks at a six-year-old dataset with a particular emphasis on air pollution data gathered from 23 Indian cities. The purpose of this investigation is to gather data and create forecasting models for air pollution. The Gaussian Naive Bayes Algorithm was used to perform substantial pre-processing on the dataset, and correlation analysis was used to identify standout features. Exploratory data analysis is used to identify specific contaminants whose presence has an immediate effect on the air quality index and uncover hidden patterns in datasets. Resampling is used to remedy the imbalance in the data problem. The AdaBoost algorithm exhibits the highest possible degree of precision. © 2023 IEEE.

关键词： AdaBoost Data Imbalance Gaussian Naive Bayes Latent Patterns

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Identification and Prediction of Sugarcane Diseases using Convolution Neural Network 1

Identification and Prediction of Sugarcane Diseases using Co...

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1st International Conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Jain, Paras Sharma, Garima Jindal, Himanshu Bharti, Monika Gupta, Vishan Kumar Singh, Mukesh Kumar Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Sehore India Graphic Era Deemed to be University Cse Department UK Dehradun India Amity University Punjab Amity School of Engineering & Technology Mohali India Galgotias College of Engineering and Technology Cse Department Greater Noida India

ISBN: (纸本)9798350376517

Sugarcane is a major global crop for sugar and bioethanol, produced mostly in tropical and subtropical countries, with Brazil and India leading producers. These countries provide 80% of the world's sugar and 40% of the bioethanol output. A multitude of environmental variables can lead sugarcane to become vulnerable to a wide range of diseases, such as Bacterial Blight, Mosaic, Red Rot, Rust, and Yellow. For diagnosis of sugarcane diseases, an accurate and reliable automated disease detection system is required. In this study, we developed convolution neural network model EfficientNetB0 with data augmentation, compared its performance, and assessed its efficacy in recognizing illnesses that affect sugarcane plants using leaf photos. The EfficientNetB0 model has precision, recall, and F1 score values of 0.9812, 0.9821, and 0.9816. With a 98.12% accuracy rate, this model is useful tool that growers may use to keep the sugarcane diseases from interfering with the sugarcane harvesting process. © 2024 IEEE.

关键词： Plant diseases

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Comprehensive Analysis of Arabic Sentiment Analysis using Lexicon and Machine Learning based Approaches 2

Comprehensive Analysis of Arabic Sentiment Analysis using Le...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Maheswari, M. Shnain, Ammar Hameed Siddiqua, Ayesha Nagaraj, H.C. Shilpa, N. Srm Institue of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India School of Engineering Sr University Department of Ece Warangal India

ISBN: (纸本)9798350372892

Arabic sentiment analysis (ASA) is a vital field in Natural Language Processing (NLP) that utilizes Machine Learning (ML) techniques to understand the complex emotional tones that are present in Arabic text. This survey discovers the use of ML in Arabic sentiment analysis, which aims to implement various methodologies for sentiment classification. The goal is to develop a robust model capable of translating nuanced sentiments expressed in different dialects and intricate linguistic structures. The methodology involves creating a labeled dataset, using preprocessing techniques, and utilizing machine learning algorithms such as linear classifiers and deep learning models. By extracting features, training the model, and evaluating its performance, the methodology improves the accuracy of sentiment evaluations. The results of this study highlight the effectiveness of machine learning models in accurately categorizing sentiments in Arabic text, as demonstrated by performance metrics such as precision, recall, and accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Medical Image Diagnosis with Convolutional Neural Networks 2

Enhancing Medical Image Diagnosis with Convolutional Neural ...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Mahadevan, K. Pushparani, S. SashiRekha, K. Sivagami, V.M. Usharani, R. Department of Computer Science and Engineering CARE College of Engineering Tamil Nadu Trichy India Department of Computer Science and Engineering Meenakshi College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Information Technology Sri Venkateswara College of Engineering Tamil Nadu Chennai India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350383867

This paper is a comparative analysis of medical image diagnosis algorithms with Convolutional Neural Networks (CNNs) and other methods;such as Support Vector Machine (SVM), Random Forest, and k-nearest Neighbors (k-NN) The study performs evaluations on datasets with multimodalities like medical images and diagnostic and various imaging modalities. Results shows that CNNs have the upper hand over traditional, static algorithms across several metrics that assesses the performance of the system such as accuracy, precision, recall, and F1. In particular, CNN precision was 90.5% while the recall was 93.7%, and the accuracy was 92.1% as well as F1-score 92.0%. In addition, the high area under the Receiver Operating Characteristic curve (AUC-ROC) of over 0.95 for the CNN also signed for its excellent discriminatory power. These outcomes only exemplify CNN usefulness in the diagnostic area because it is an exact and accurate method for analyzing medical images. Although the nets CNNs are deployed in clinical cases may help address concerns regarding interpretability, compliance with regulations, and ethical opinions, such issues still exist and require further exploration. Overall, this research takes the first steps in directing algorithmic approach towards medical diagnosis and show the feasibility in deployment of CNNs as inseparable pieces of equipment in modern healthcare. © 2024 IEEE.

关键词： Random forests

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Stable Learning via Triplex Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5267-5276页

作者： Yang, Shuai Jiang, Tingting Dang, Qianlong Gu, Lichuan Wu, Xindong Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing Hefei230036 China Northwest A & F University College of Science Yangling712100 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Stable learning aims to learn a model that generalizes well to arbitrary unseen target domain by leveraging a single source domain. Recent advances in stable learning have focused on balancing the distribution of confounders for each feature to eliminate spurious correlations. However, previous studies treat all features equally without considering the difficulties of confounder balancing associated with different features, and regard irrelevant features as confounders, deteriorating generalization performance. To tackle these issues, this article proposes a novel triplex learning (TriL) based stable learning algorithm, which performs sample reweighting, causal feature selection, and representation learning to remove spurious correlations. Specifically, first, TriL adaptively assigns weights to the confounder balancing term of each feature in accordance with the difficulties of confounder balancing, and aligns the confounder distribution of each feature by learning a group of sample weights. Second, TriL integrates the sample weights into a weighted cross-entropy model to compute causal effects of features for excluding irrelevant features from the confounder set. Finally, TriL relearns a set of sample weights and uses them to guide a new supervised dual-autoencoder containing two classifiers to learn feature representations. TriL forces the results of two classifiers to remain consistent for removing spurious correlations by using a cross-classifier consistency regularization. Extensive experiments on synthetic and two real-world datasets show the superiority of TriL compared with seven methods. © 2024 IEEE.

关键词： Feature Selection

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Enhancing Wound Care in Healthcare Systems with Logistic Regression for Infection Detection 1

Enhancing Wound Care in Healthcare Systems with Logistic Reg...

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1st International Conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Ramalingam, L. Amarnath, Raveendra N. Arun, V. Sendhilkumar, N.C. Ravi, Rupavathy Srinivasan, C. Loyola Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Mphasis Corporation MelissaTX United States SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Hyderabad India Agni College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

In healthcare systems, wound care management is essential for reducing infections and promoting faster recovery. To improve wound care, this paper proposes a data-driven method that combines machine learning (ML), more particularly, Logistic Regression (LR), with Internet of Things (IoT) sensors to identify early infections. The system provides real-time data streams to healthcare providers by continually monitoring important wound parameters, including temperature, moisture, pH levels, and exudate presence via IoT-enabled sensors. Using LR, the system predicts the potential of wound infections based on these physiological indicators, allowing for more effective patient care and early treatments. When tested for accuracy and dependability in infection prediction, the proposed model shows significant detection skills with few false positives. This approach optimizes clinical processes by reducing the need for manual wound examination, enhancing patient safety, and lowering the possibility of complications from infections. Wound care management might improve patient outcomes and expedite infection control measures in medical institutions. © 2024 IEEE.

关键词： Logistic regression

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A Survey on Event Tracking in Social Media Data Streams

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Big Data Mining and Analytics 2024年第1期7卷 217-243页

作者： Zixuan Han Leilei Shi Lu Liu Liang Jiang Jiawei Fang Fanyuan Lin Jinjuan Zhang John Panneerselvam Nick Antonopoulos School of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu UniversityZhenjiang 212013China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Ocean College Jiangsu University of Science and TechnologyZhenjiang 212100China University Executive Office Edinburgh Napier UniversityEdinburghEH114BNUK

Social networks are inevitable parts of our daily life,where an unprecedented amount of complex data corresponding to a diverse range of applications are *** such,it is imperative to conduct research on social events and patterns from the perspectives of conventional sociology to optimize services that originate from social *** tracking in social networks finds various applications,such as network security and societal governance,which involves analyzing data generated by user groups on social networks in real ***,as deep learning techniques continue to advance and make important breakthroughs in various fields,researchers are using this technology to progressively optimize the effectiveness of Event Detection(ED)and tracking *** this regard,this paper presents an in-depth comprehensive review of the concept and methods involved in ED and tracking in social *** introduce mainstream event tracking methods,which involve three primary technical steps:ED,event propagation,and event ***,we introduce benchmark datasets and evaluation metrics for ED and tracking,which allow comparative analysis on the performance of mainstream ***,we present a comprehensive analysis of the main research findings and existing limitations in this field,as well as future research prospects and challenges.

关键词： Event Detection(ED) event propagation event evolution social networks

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“The Devil You Know”: Barriers and Opportunities for Co-Designing Microclimate Sensors, A Case Study of Manoomi

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ACM Journal on computing and Sustainable Societies 2024年第3期2卷 1-30页

作者： Eric Greenlee Blaine Rothrock Hyeonwook Kim Ellen Zegura Josiah Hester College of Computing Georgia Institute of Technology Atlanta United States McCormick School of Engineering Northwestern University Evanston United States College of Computing Georgia Institute of Technology Atlanta United States

Current environmental challenges have profound local consequences and often benefit from the collection of fine-grained microclimate data. Advances in wireless sensor networks and the Internet of Things have led to technologies nominally suited to support remote sensing; however, in practice long-running deployments of in-field environmental sensors are rare. Field conditions are often remote and culturally sensitive, with limited power, Internet, transportation, and human infrastructure; advances in device technology alone will not suffice. We ask how communities, Internet of Things researchers, government, and other interested parties can work together to co-design useful, low burden, sustainability-focused infrastructure. Toward this end, we conducted 11 semi-structured interviews with 13 experts who use or rely on environmental sensing technology. To complement our interview data, we engaged in three months of participant observation while immersed in organizations specifically working toward manoomin (wild rice) conservation. We make two primary contributions. First, we confirm and enrich a five-stage model, the microclimate sensor lifecycle, focusing on desired features and persistent challenges. Second, we outline a space for co-design of microclimate sensors with emphasis on the cost of experience, the generally unaddressed issue of technical usability in the messy field, and the opportunity for community engagement to improve technical design and outcomes. Furthermore, we discuss future design opportunities, recommendations, and challenges in the microclimate sensor design, deployment, and sustainability space.

关键词： Environmental Sensors Co-design Community Engagement

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